Overview

Dataset statistics

Number of variables15
Number of observations999
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory124.9 KiB
Average record size in memory128.0 B

Variable types

Numeric8
Categorical3
DateTime2
Text2

Alerts

FraudIndicator is highly imbalanced (73.5%)Imbalance
SuspiciousFlag is highly imbalanced (83.1%)Imbalance
TransactionID is uniformly distributedUniform
TransactionID has unique valuesUnique
TransactionAmount has unique valuesUnique
AnomalyScore has unique valuesUnique
Timestamp has unique valuesUnique
Amount has unique valuesUnique

Reproduction

Analysis started2024-01-01 15:30:43.774019
Analysis finished2024-01-01 15:31:11.938807
Duration28.16 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

TransactionID
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct999
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.76276
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-01-01T15:31:12.101602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50.9
Q1251.5
median501
Q3750.5
95-th percentile950.1
Maximum1000
Range999
Interquartile range (IQR)499

Descriptive statistics

Standard deviation288.84449
Coefficient of variation (CV)0.57680903
Kurtosis-1.199499
Mean500.76276
Median Absolute Deviation (MAD)250
Skewness-0.0019809945
Sum500262
Variance83431.137
MonotonicityNot monotonic
2024-01-01T15:31:12.389409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
611 1
 
0.1%
508 1
 
0.1%
460 1
 
0.1%
461 1
 
0.1%
463 1
 
0.1%
769 1
 
0.1%
879 1
 
0.1%
465 1
 
0.1%
688 1
 
0.1%
Other values (989) 989
99.0%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1000 1
0.1%
999 1
0.1%
998 1
0.1%
997 1
0.1%
996 1
0.1%
995 1
0.1%
994 1
0.1%
993 1
0.1%
992 1
0.1%
991 1
0.1%

FraudIndicator
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.6 KiB
0
954 
1
 
45

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters999
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 954
95.5%
1 45
 
4.5%

Length

2024-01-01T15:31:12.612249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T15:31:13.056133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 954
95.5%
1 45
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 954
95.5%
1 45
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 999
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 954
95.5%
1 45
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 999
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 954
95.5%
1 45
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 999
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 954
95.5%
1 45
 
4.5%

Category
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size15.6 KiB
Other
210 
Food
204 
Travel
198 
Online
196 
Retail
191 

Length

Max length6
Median length6
Mean length5.3813814
Min length4

Characters and Unicode

Total characters5376
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowFood
3rd rowOnline
4th rowTravel
5th rowRetail

Common Values

ValueCountFrequency (%)
Other 210
21.0%
Food 204
20.4%
Travel 198
19.8%
Online 196
19.6%
Retail 191
19.1%

Length

2024-01-01T15:31:13.241241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T15:31:13.517939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
other 210
21.0%
food 204
20.4%
travel 198
19.8%
online 196
19.6%
retail 191
19.1%

Most occurring characters

ValueCountFrequency (%)
e 795
14.8%
l 585
10.9%
r 408
7.6%
o 408
7.6%
O 406
7.6%
t 401
7.5%
n 392
 
7.3%
a 389
 
7.2%
i 387
 
7.2%
h 210
 
3.9%
Other values (5) 995
18.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4377
81.4%
Uppercase Letter 999
 
18.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 795
18.2%
l 585
13.4%
r 408
9.3%
o 408
9.3%
t 401
9.2%
n 392
9.0%
a 389
8.9%
i 387
8.8%
h 210
 
4.8%
d 204
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
O 406
40.6%
F 204
20.4%
T 198
19.8%
R 191
19.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 5376
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 795
14.8%
l 585
10.9%
r 408
7.6%
o 408
7.6%
O 406
7.6%
t 401
7.5%
n 392
 
7.3%
a 389
 
7.2%
i 387
 
7.2%
h 210
 
3.9%
Other values (5) 995
18.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 795
14.8%
l 585
10.9%
r 408
7.6%
o 408
7.6%
O 406
7.6%
t 401
7.5%
n 392
 
7.3%
a 389
 
7.2%
i 387
 
7.2%
h 210
 
3.9%
Other values (5) 995
18.5%

TransactionAmount
Real number (ℝ)

UNIQUE 

Distinct999
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.884419
Minimum10.057864
Maximum99.784323
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-01-01T15:31:13.793840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10.057864
5-th percentile13.864613
Q133.881084
median55.999203
Q377.591591
95-th percentile96.516388
Maximum99.784323
Range89.72646
Interquartile range (IQR)43.710507

Descriptive statistics

Standard deviation26.088726
Coefficient of variation (CV)0.46683363
Kurtosis-1.1627959
Mean55.884419
Median Absolute Deviation (MAD)21.935513
Skewness-0.046907706
Sum55828.534
Variance680.62164
MonotonicityNot monotonic
2024-01-01T15:31:14.052517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79.41360746 1
 
0.1%
35.53622466 1
 
0.1%
91.66415279 1
 
0.1%
23.30461399 1
 
0.1%
57.62801475 1
 
0.1%
71.39490629 1
 
0.1%
89.52999305 1
 
0.1%
44.37912364 1
 
0.1%
58.74144775 1
 
0.1%
39.32062115 1
 
0.1%
Other values (989) 989
99.0%
ValueCountFrequency (%)
10.05786365 1
0.1%
10.1432748 1
0.1%
10.14333882 1
0.1%
10.1900649 1
0.1%
10.23072664 1
0.1%
10.24979371 1
0.1%
10.31780692 1
0.1%
10.62508533 1
0.1%
10.8310472 1
0.1%
10.90584595 1
0.1%
ValueCountFrequency (%)
99.78432334 1
0.1%
99.6010349 1
0.1%
99.52235673 1
0.1%
99.47675323 1
0.1%
99.39547483 1
0.1%
99.37033289 1
0.1%
99.26625704 1
0.1%
99.16801169 1
0.1%
99.13454678 1
0.1%
99.09515145 1
0.1%

AnomalyScore
Real number (ℝ)

UNIQUE 

Distinct999
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49268467
Minimum0.000233707
Maximum0.99904734
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-01-01T15:31:14.339032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.000233707
5-th percentile0.042458958
Q10.25224979
median0.49149978
Q30.74259373
95-th percentile0.94839381
Maximum0.99904734
Range0.99881363
Interquartile range (IQR)0.49034394

Descriptive statistics

Standard deviation0.28828554
Coefficient of variation (CV)0.58513194
Kurtosis-1.1952247
Mean0.49268467
Median Absolute Deviation (MAD)0.24342938
Skewness0.0061316224
Sum492.19199
Variance0.083108553
MonotonicityNot monotonic
2024-01-01T15:31:14.792873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.686699464 1
 
0.1%
0.646370916 1
 
0.1%
0.210671702 1
 
0.1%
0.414738295 1
 
0.1%
0.41545934 1
 
0.1%
0.248070399 1
 
0.1%
0.43010061 1
 
0.1%
0.334052793 1
 
0.1%
0.759861663 1
 
0.1%
0.892815846 1
 
0.1%
Other values (989) 989
99.0%
ValueCountFrequency (%)
0.000233707 1
0.1%
0.000312921 1
0.1%
0.000929961 1
0.1%
0.001311236 1
0.1%
0.003313462 1
0.1%
0.003373251 1
0.1%
0.005158218 1
0.1%
0.006120114 1
0.1%
0.006331516 1
0.1%
0.006563153 1
0.1%
ValueCountFrequency (%)
0.999047341 1
0.1%
0.997339633 1
0.1%
0.997038134 1
0.1%
0.995561768 1
0.1%
0.99233785 1
0.1%
0.99192332 1
0.1%
0.991626339 1
0.1%
0.989993343 1
0.1%
0.989508393 1
0.1%
0.986039738 1
0.1%

Timestamp
Date

UNIQUE 

Distinct999
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size15.6 KiB
Minimum2022-01-01 00:00:00
Maximum2022-12-01 23:00:00
2024-01-01T15:31:15.281266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:15.748609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

MerchantID
Real number (ℝ)

Distinct650
Distinct (%)65.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2504.975
Minimum2001
Maximum3000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-01-01T15:31:16.212787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2053.7
Q12255.5
median2501
Q32761.5
95-th percentile2946
Maximum3000
Range999
Interquartile range (IQR)506

Descriptive statistics

Standard deviation288.42912
Coefficient of variation (CV)0.11514252
Kurtosis-1.2130552
Mean2504.975
Median Absolute Deviation (MAD)253
Skewness-0.0054961984
Sum2502470
Variance83191.357
MonotonicityNot monotonic
2024-01-01T15:31:16.701150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2597 4
 
0.4%
2444 4
 
0.4%
2239 4
 
0.4%
2850 4
 
0.4%
2479 4
 
0.4%
2631 4
 
0.4%
2736 4
 
0.4%
2200 4
 
0.4%
2112 4
 
0.4%
2081 4
 
0.4%
Other values (640) 959
96.0%
ValueCountFrequency (%)
2001 1
 
0.1%
2004 2
0.2%
2005 1
 
0.1%
2007 4
0.4%
2008 1
 
0.1%
2010 2
0.2%
2011 1
 
0.1%
2012 1
 
0.1%
2013 2
0.2%
2014 1
 
0.1%
ValueCountFrequency (%)
3000 1
 
0.1%
2998 1
 
0.1%
2997 2
0.2%
2995 1
 
0.1%
2994 1
 
0.1%
2993 1
 
0.1%
2992 2
0.2%
2991 1
 
0.1%
2989 1
 
0.1%
2984 3
0.3%

Amount
Real number (ℝ)

UNIQUE 

Distinct999
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.395063
Minimum10.006933
Maximum99.88741
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-01-01T15:31:17.125856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10.006933
5-th percentile14.18634
Q134.48377
median57.868203
Q375.867802
95-th percentile95.224008
Maximum99.88741
Range89.880477
Interquartile range (IQR)41.384032

Descriptive statistics

Standard deviation25.083366
Coefficient of variation (CV)0.45280869
Kurtosis-1.0980006
Mean55.395063
Median Absolute Deviation (MAD)20.444505
Skewness-0.074880452
Sum55339.667
Variance629.17524
MonotonicityNot monotonic
2024-01-01T15:31:17.578863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55.53033443 1
 
0.1%
88.34186423 1
 
0.1%
14.13938756 1
 
0.1%
83.69207704 1
 
0.1%
27.57822485 1
 
0.1%
44.86059946 1
 
0.1%
39.16310962 1
 
0.1%
84.55259194 1
 
0.1%
96.7704106 1
 
0.1%
23.62371874 1
 
0.1%
Other values (989) 989
99.0%
ValueCountFrequency (%)
10.00693256 1
0.1%
10.04748839 1
0.1%
10.58886273 1
0.1%
10.64214063 1
0.1%
10.66165127 1
0.1%
10.80241294 1
0.1%
10.86384497 1
0.1%
10.86708774 1
0.1%
10.9330459 1
0.1%
10.98045532 1
0.1%
ValueCountFrequency (%)
99.88740974 1
0.1%
99.87930231 1
0.1%
99.83263446 1
0.1%
99.77535839 1
0.1%
99.71923371 1
0.1%
99.61607032 1
0.1%
99.57766068 1
0.1%
99.24329386 1
0.1%
99.1004274 1
0.1%
98.80425008 1
0.1%

CustomerID
Real number (ℝ)

Distinct636
Distinct (%)63.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1497.0991
Minimum1001
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-01-01T15:31:17.996767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1052.9
Q11239
median1501
Q31739.5
95-th percentile1958.2
Maximum2000
Range999
Interquartile range (IQR)500.5

Descriptive statistics

Standard deviation288.98653
Coefficient of variation (CV)0.193031
Kurtosis-1.1753761
Mean1497.0991
Median Absolute Deviation (MAD)250
Skewness0.028045889
Sum1495602
Variance83513.216
MonotonicityNot monotonic
2024-01-01T15:31:18.395845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1825 6
 
0.6%
1424 6
 
0.6%
1666 6
 
0.6%
1322 5
 
0.5%
1311 5
 
0.5%
1618 5
 
0.5%
1480 4
 
0.4%
1117 4
 
0.4%
1503 4
 
0.4%
1405 4
 
0.4%
Other values (626) 950
95.1%
ValueCountFrequency (%)
1001 1
0.1%
1003 1
0.1%
1004 2
0.2%
1005 1
0.1%
1007 1
0.1%
1008 1
0.1%
1009 2
0.2%
1012 2
0.2%
1014 1
0.1%
1016 1
0.1%
ValueCountFrequency (%)
2000 3
0.3%
1999 2
0.2%
1998 1
 
0.1%
1997 1
 
0.1%
1996 1
 
0.1%
1995 4
0.4%
1994 2
0.2%
1993 1
 
0.1%
1992 1
 
0.1%
1991 2
0.2%

Name
Text

Distinct636
Distinct (%)63.7%
Missing0
Missing (%)0.0%
Memory size15.6 KiB
2024-01-01T15:31:19.147017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters12987
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique379 ?
Unique (%)37.9%

Sample

1st rowCustomer 1952
2nd rowCustomer 1952
3rd rowCustomer 1027
4th rowCustomer 1955
5th rowCustomer 1955
ValueCountFrequency (%)
customer 999
50.0%
1424 6
 
0.3%
1666 6
 
0.3%
1825 6
 
0.3%
1322 5
 
0.3%
1311 5
 
0.3%
1618 5
 
0.3%
1995 4
 
0.2%
1796 4
 
0.2%
1244 4
 
0.2%
Other values (627) 954
47.7%
2024-01-01T15:31:20.172692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1313
10.1%
e 999
 
7.7%
u 999
 
7.7%
999
 
7.7%
r 999
 
7.7%
C 999
 
7.7%
m 999
 
7.7%
o 999
 
7.7%
t 999
 
7.7%
s 999
 
7.7%
Other values (9) 2683
20.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6993
53.8%
Decimal Number 3996
30.8%
Space Separator 999
 
7.7%
Uppercase Letter 999
 
7.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1313
32.9%
5 321
 
8.0%
6 316
 
7.9%
2 315
 
7.9%
9 307
 
7.7%
0 301
 
7.5%
7 291
 
7.3%
4 287
 
7.2%
3 274
 
6.9%
8 271
 
6.8%
Lowercase Letter
ValueCountFrequency (%)
e 999
14.3%
u 999
14.3%
r 999
14.3%
m 999
14.3%
o 999
14.3%
t 999
14.3%
s 999
14.3%
Space Separator
ValueCountFrequency (%)
999
100.0%
Uppercase Letter
ValueCountFrequency (%)
C 999
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7992
61.5%
Common 4995
38.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1313
26.3%
999
20.0%
5 321
 
6.4%
6 316
 
6.3%
2 315
 
6.3%
9 307
 
6.1%
0 301
 
6.0%
7 291
 
5.8%
4 287
 
5.7%
3 274
 
5.5%
Latin
ValueCountFrequency (%)
e 999
12.5%
u 999
12.5%
r 999
12.5%
C 999
12.5%
m 999
12.5%
o 999
12.5%
t 999
12.5%
s 999
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12987
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1313
10.1%
e 999
 
7.7%
u 999
 
7.7%
999
 
7.7%
r 999
 
7.7%
C 999
 
7.7%
m 999
 
7.7%
o 999
 
7.7%
t 999
 
7.7%
s 999
 
7.7%
Other values (9) 2683
20.7%

Age
Real number (ℝ)

Distinct47
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.847848
Minimum18
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-01-01T15:31:20.464164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q129
median39
Q351
95-th percentile61
Maximum64
Range46
Interquartile range (IQR)22

Descriptive statistics

Standard deviation13.08148
Coefficient of variation (CV)0.32828574
Kurtosis-1.1244721
Mean39.847848
Median Absolute Deviation (MAD)11
Skewness0.10463238
Sum39808
Variance171.12512
MonotonicityNot monotonic
2024-01-01T15:31:20.735137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
33 37
 
3.7%
47 35
 
3.5%
37 31
 
3.1%
18 29
 
2.9%
35 29
 
2.9%
31 28
 
2.8%
21 27
 
2.7%
27 27
 
2.7%
29 27
 
2.7%
26 25
 
2.5%
Other values (37) 704
70.5%
ValueCountFrequency (%)
18 29
2.9%
19 18
1.8%
20 15
1.5%
21 27
2.7%
22 20
2.0%
23 23
2.3%
24 14
1.4%
25 14
1.4%
26 25
2.5%
27 27
2.7%
ValueCountFrequency (%)
64 9
 
0.9%
63 18
1.8%
62 17
1.7%
61 16
1.6%
60 22
2.2%
59 17
1.7%
58 23
2.3%
57 18
1.8%
56 14
1.4%
55 24
2.4%
Distinct636
Distinct (%)63.7%
Missing0
Missing (%)0.0%
Memory size15.6 KiB
2024-01-01T15:31:21.332123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters11988
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique379 ?
Unique (%)37.9%

Sample

1st rowAddress 1952
2nd rowAddress 1952
3rd rowAddress 1027
4th rowAddress 1955
5th rowAddress 1955
ValueCountFrequency (%)
address 999
50.0%
1424 6
 
0.3%
1666 6
 
0.3%
1825 6
 
0.3%
1322 5
 
0.3%
1311 5
 
0.3%
1618 5
 
0.3%
1995 4
 
0.2%
1796 4
 
0.2%
1244 4
 
0.2%
Other values (627) 954
47.7%
2024-01-01T15:31:22.164841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 1998
16.7%
s 1998
16.7%
1 1313
11.0%
A 999
8.3%
r 999
8.3%
e 999
8.3%
999
8.3%
5 321
 
2.7%
6 316
 
2.6%
2 315
 
2.6%
Other values (6) 1731
14.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5994
50.0%
Decimal Number 3996
33.3%
Uppercase Letter 999
 
8.3%
Space Separator 999
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1313
32.9%
5 321
 
8.0%
6 316
 
7.9%
2 315
 
7.9%
9 307
 
7.7%
0 301
 
7.5%
7 291
 
7.3%
4 287
 
7.2%
3 274
 
6.9%
8 271
 
6.8%
Lowercase Letter
ValueCountFrequency (%)
d 1998
33.3%
s 1998
33.3%
r 999
16.7%
e 999
16.7%
Uppercase Letter
ValueCountFrequency (%)
A 999
100.0%
Space Separator
ValueCountFrequency (%)
999
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6993
58.3%
Common 4995
41.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1313
26.3%
999
20.0%
5 321
 
6.4%
6 316
 
6.3%
2 315
 
6.3%
9 307
 
6.1%
0 301
 
6.0%
7 291
 
5.8%
4 287
 
5.7%
3 274
 
5.5%
Latin
ValueCountFrequency (%)
d 1998
28.6%
s 1998
28.6%
A 999
14.3%
r 999
14.3%
e 999
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11988
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 1998
16.7%
s 1998
16.7%
1 1313
11.0%
A 999
8.3%
r 999
8.3%
e 999
8.3%
999
8.3%
5 321
 
2.7%
6 316
 
2.6%
2 315
 
2.6%
Other values (6) 1731
14.4%

AccountBalance
Real number (ℝ)

Distinct636
Distinct (%)63.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5719.38
Minimum1056.3012
Maximum9999.7762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-01-01T15:31:22.470137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1056.3012
5-th percentile1669.6768
Q13494.5829
median5760.8876
Q37926.3382
95-th percentile9449.859
Maximum9999.7762
Range8943.4751
Interquartile range (IQR)4431.7553

Descriptive statistics

Standard deviation2538.7559
Coefficient of variation (CV)0.44388656
Kurtosis-1.1915559
Mean5719.38
Median Absolute Deviation (MAD)2228.2299
Skewness-0.099727124
Sum5713660.6
Variance6445281.5
MonotonicityNot monotonic
2024-01-01T15:31:22.747629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4886.010825 6
 
0.6%
4344.549864 6
 
0.6%
9674.062164 6
 
0.6%
9411.508717 5
 
0.5%
9253.478917 5
 
0.5%
6741.429339 5
 
0.5%
7269.756044 4
 
0.4%
7288.036638 4
 
0.4%
4370.431522 4
 
0.4%
9246.516979 4
 
0.4%
Other values (626) 950
95.1%
ValueCountFrequency (%)
1056.301181 1
 
0.1%
1069.843868 1
 
0.1%
1087.073831 1
 
0.1%
1097.423046 2
0.2%
1098.721299 1
 
0.1%
1106.441844 1
 
0.1%
1111.772167 2
0.2%
1117.747401 1
 
0.1%
1150.295949 3
0.3%
1153.123295 1
 
0.1%
ValueCountFrequency (%)
9999.776239 1
0.1%
9996.991525 1
0.1%
9992.495729 1
0.1%
9974.701765 1
0.1%
9960.172528 2
0.2%
9938.922919 2
0.2%
9934.498597 1
0.1%
9927.157719 2
0.2%
9922.291973 1
0.1%
9911.337339 1
0.1%
Distinct636
Distinct (%)63.7%
Missing0
Missing (%)0.0%
Memory size15.6 KiB
Minimum2022-01-01 00:00:00
Maximum2024-09-26 00:00:00
2024-01-01T15:31:23.008599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:23.271907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

SuspiciousFlag
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.6 KiB
0
974 
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters999
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 974
97.5%
1 25
 
2.5%

Length

2024-01-01T15:31:23.478917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T15:31:23.669797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 974
97.5%
1 25
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 974
97.5%
1 25
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 999
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 974
97.5%
1 25
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common 999
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 974
97.5%
1 25
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 999
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 974
97.5%
1 25
 
2.5%

Interactions

2024-01-01T15:31:09.463274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:30:50.131380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:30:53.196239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:30:56.002381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:00.175182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:04.230385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:05.942908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:07.740647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:09.677782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:30:50.498840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:30:53.581509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:30:56.414201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:00.717185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:04.440216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:06.172183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:07.960402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:09.877858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:30:50.976659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:30:54.000149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:30:56.798746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:01.221314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:04.665500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:06.390958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:08.169834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:10.086429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:30:51.342859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:30:54.387260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:30:57.186429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:01.901083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:04.872110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:06.610303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:08.388480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:10.311678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:30:51.679552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:30:54.762001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:30:57.838521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:02.462739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:05.106497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:06.839148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:08.595176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:10.506610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:30:52.022886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:30:55.021297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:30:58.365045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:03.001454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:05.324040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:07.064278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:08.799629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:10.708687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:30:52.411212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:30:55.367704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:30:59.239492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:03.363293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:05.538681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:07.308197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:09.029242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:10.924074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:30:52.811350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:30:55.707228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:30:59.778861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:03.680295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:05.742687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:07.528412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-01T15:31:09.254566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-01-01T15:31:23.851208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AccountBalanceAgeAmountAnomalyScoreCategoryCustomerIDFraudIndicatorMerchantIDSuspiciousFlagTransactionAmountTransactionID
AccountBalance1.0000.0160.005-0.0110.075-0.0590.0000.0230.1310.043-0.057
Age0.0161.000-0.035-0.0260.000-0.0050.0000.0140.121-0.031-0.010
Amount0.005-0.0351.000-0.0060.000-0.0300.0000.0090.000-0.0020.024
AnomalyScore-0.011-0.026-0.0061.0000.0380.0090.0560.0720.071-0.0400.078
Category0.0750.0000.0000.0381.0000.0200.0000.0070.045-0.013-0.017
CustomerID-0.059-0.005-0.0300.0090.0201.0000.0000.0220.102-0.0650.010
FraudIndicator0.0000.0000.0000.0560.0000.0001.000-0.0010.028-0.034-0.036
MerchantID0.0230.0140.0090.0720.0070.022-0.0011.0000.0290.0290.017
SuspiciousFlag0.1310.1210.0000.0710.0450.1020.0280.0291.0000.005-0.039
TransactionAmount0.043-0.031-0.002-0.040-0.013-0.065-0.0340.0290.0051.000-0.014
TransactionID-0.057-0.0100.0240.078-0.0170.010-0.0360.017-0.039-0.0141.000

Missing values

2024-01-01T15:31:11.279583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-01T15:31:11.736271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TransactionIDFraudIndicatorCategoryTransactionAmountAnomalyScoreTimestampMerchantIDAmountCustomerIDNameAgeAddressAccountBalanceLastLoginSuspiciousFlag
010Other79.4136070.68669901-01-2022 00:00270155.5303341952Customer 195250Address 19522869.6899122024-08-090
18950Food90.4220260.04081707-02-2022 06:00214953.6838981952Customer 195250Address 19522869.6899122024-08-090
220Online12.0530870.08174901-01-2022 01:00207012.8811801027Customer 102746Address 10279527.9471072022-01-270
330Travel33.3103570.02385701-01-2022 02:00223850.1763221955Customer 195534Address 19559288.3555252024-08-120
45440Retail20.2953480.47681223-01-2022 15:00264352.9911841955Customer 195534Address 19559288.3555252024-08-120
540Travel46.1211170.87699401-01-2022 03:00287941.6340011796Customer 179633Address 17965588.0499422024-03-060
61090Food66.5143720.78242805-01-2022 12:00250848.7379591796Customer 179633Address 17965588.0499422024-03-060
75940Other10.1432750.12327825-01-2022 17:00217622.0132101796Customer 179633Address 17965588.0499422024-03-060
89850Online99.5223570.85896011-02-2022 00:00201087.0116191796Customer 179633Address 17965588.0499422024-03-060
950Other54.0516180.03405901-01-2022 04:00296678.1228531946Customer 194618Address 19467324.7853322024-08-030
TransactionIDFraudIndicatorCategoryTransactionAmountAnomalyScoreTimestampMerchantIDAmountCustomerIDNameAgeAddressAccountBalanceLastLoginSuspiciousFlag
9899770Travel15.9104890.61337110-02-2022 16:00266168.4670811153Customer 115326Address 11537795.5858072022-06-020
9909780Online27.0162500.56327910-02-2022 17:00298341.6138441055Customer 105524Address 10551611.5383152022-02-240
9919790Other79.7234480.03917010-02-2022 18:00291052.3381081020Customer 102055Address 10203552.8551472022-01-200
9929800Travel36.0212520.50726110-02-2022 19:00266659.3088671421Customer 142141Address 14216341.7437972023-02-250
9939840Online34.4895070.16939810-02-2022 23:00259349.4500721884Customer 188455Address 18846010.6489222024-06-020
9949860Other89.9723620.28560311-02-2022 01:00203629.3288151706Customer 170645Address 17061678.5149682023-12-070
9959890Food17.8484810.02954311-02-2022 04:00228420.4438111312Customer 131226Address 13126018.4436472022-11-080
9969960Food89.4570590.26677811-02-2022 11:00215019.8767501411Customer 141119Address 14111290.3230592023-02-150
9979970Retail47.9580300.51248311-02-2022 12:00288896.2997921566Customer 156639Address 15667067.8316092023-07-200
9989980Food64.2100460.36774011-02-2022 13:00203775.1644591654Customer 165451Address 16549088.7383592023-10-160